R Notebook

# base working

library(dplyr)
library(tidyverse)
library(readr)
library(ggplot2)
library(dashHtmlComponents)
library(dashCoreComponents)
library(plotly)
library(dash)
library(purrr)

suf_data <- read.csv(file = 'data/processed/sufang_clean_df.csv')
j_data <- read_csv("data/processed/jasmine_df.csv")

New names:
* `` -> ...1

Rows: 3939 Columns: 7
── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (4): title, cast, listed_in, rating
dbl (3): ...1, cast_count, release_year

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

data <- read_csv("data/processed/clean_df.csv")

New names:
* `` -> ...1

Rows: 3939 Columns: 13
── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (10): title, director, cast, country, date_added, rating, duration, listed_in, description, type
dbl  (3): ...1, show_id, release_year

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

j_data <- j_data %>% filter(is.na(j_data$cast_count) == FALSE)

cast_data <- j_data %>% 
  group_by(release_year) %>%
  summarize(mean_cast_count = mean(cast_count))

 p <- cast_data %>%
      ggplot(aes(x=release_year,
                 y=mean_cast_count)) +
      geom_point(fill= "red2", color = "black", shape = 21, size = 3) +
      ggtitle("Average Cast Size Per Year") +
      xlab("Release Year") +
      ylab("Avg. Cast Size") +
      xlim(1942, 2020) +
      theme(plot.title = element_text(hjust = 0.5, color = "white"),
            panel.background = element_blank(),
            panel.grid = element_line(color = "gray90"),
            axis.line = element_line(colour = "black"),
            plot.background = element_rect(fill = '#171614', colour = '#171614'),
            axis.text = element_text(color="white"),
            axis.title.x = element_text(color="white"),
            axis.title.y = element_text(color="white")
      )
    ggplotly(p + aes(text = release_year), tooltip = 'release_year')  %>% layout(plot_bgcolor = '000000')

c("#CE2626", 
  "#820263", 
  "#FBBA72", 
  "#EFAAC4", 
  "#A56124", 
  "#D30C7B",
  "#520F2A", 
  "#FF3C38", 
  "#FF8C42", 
  "#F991CC", 
  "#A05BFA", 
  "#9F2042")

summarize(mean_cast_count = mean(cast_count))

app <- Dash$new(external_stylesheets = dbcThemes$BOOTSTRAP)


app$layout(
  dbcContainer(
    list(
    
    # title card 
    dbcRow(
      list(
        dbcCard(
          dbcCardBody(
            list(h4("Netflix Movie Dashboard: Visualize movie trends on the world's most popular streaming platform!", className = "card-title")),
            dbcCol(
            style=list("font-weight"="bold", "font-size"="85%"),
            ), # dbcCol
          ), # dbcCardBody
          color ="dark", 
          inverse=TRUE
        ) #dbcCard
    ), # list
    ), # dbcRow
    
    # first row
     dbcRow(
      list(
      
       # Jasmine's plot
         dbcCol( 
          dbcCard(
            dbcCardBody(
              div(
                 list(
                  dccGraph(id='scatter'),
                  htmlLabel('Year Range'),
                  
                  dccSlider(
                    id='xslider',
                    min=1942,
                    max=2019,
                    marks = list(
                      '1942' = '1942',
                      '1962' = '1962',
                      '1982' = '1982',
                      '2002' = '2002',
                      '2019'= '2019'
                      ),
                  value=2002)
              )
              
            ) # html
          ), # dbcCardBody
        color="dark"
        ), # dbcCard
        md=6,
      ), # Jasmine dbcCol
      
      # Mahsa plot
      
      dbcCol(
          dbcCard(
            dbcCardBody(
              div(
                list(
                  dccGraph(id='plot_line'),

                  dccDropdown(
                    id='rating-select',
                    options = data$rating %>%
                      purrr::map(function(rating,pop) list(label = rating, value = rating)),
                    value=list("TV-G","TV-MA", "TV-14","TV-Y7"),
                    multi=TRUE
                    ),

                  dccRangeSlider(
                    id='my-range-slider',
                    min=1942,
                    max=2020,
                    marks =
                      list(
                        '1942' = '1942',
                        '1960' = '1960',
                        '1980' = '1980',
                        '2000' = '2000',
                        '2020' = '2020'
                      ),
                    value=list(2003, 2020)
                  )
                ) # list
              ) # div
            ), # dbcCardBody
            color="dark"
          ), # dbcCard
    md=6), # close dbcCol
      
    dbcRow(
      dbcCol(
        dbcCard(
         dbcCardBody(
              div(
                
                ##
                
      list(
      dccGraph(id='plot-area'),
      htmlLabel("Year"),
      dccRangeSlider(id='year3',
                min = min(suf_data$release_year),
                max= max(suf_data$release_year),
                value=list(1995, 2020),
                marks=list(
                  '1970'= "1970",
                  '1975'= "1975",
                  '1980'= "1980",
                  '1985'= "1985",
                  '1990'= "1990",
                  '1995'= "1995",
                  '2000'= "2000",
                  '2005'= "2005",
                  '2010'= "2010",
                  '2015'= "2015",
                  '2020'= "2020"
                )
                ),
      htmlLabel("Duration"),
      dccRangeSlider(id='duration3',
                min = min(suf_data$duration),
                max = max(suf_data$duration),
                value=list(60, 120),
                marks=list(
                  '10'=  "10 min",
                  '30'= "30 min",
                  '50'= "50 min",
                  '70'= "70 min",
                  '90'= "90 min",
                  '110'= "110 min",
                  '130'= "130 min",
                  '150'= "150 min",
                  '170'= "170 min",
                  '190'= "190 min",
                  '210'= "210 min",
                  '230'= "230 min")
          )
        )
                
                ###
              ) # div
         ), # dbcCardBody
      color="dark"
        ) # dbcCard
      )
    )

      ) # close list
    ) # first row
    ), # close list
    style=list(backgroundColor = "#000000")
  ) # container  
) 

# jasmine callback 
app$callback(
  output('scatter', 'figure'),
  list(input('xslider', 'value')),
  function(xcol) {

    p <- cast_data %>%
      ggplot(aes(x=release_year,
                 y=mean_cast_count)) +
      geom_point(fill= "red2", color = "black", shape = 21, size = 3) +
      ggtitle("Average Cast Size Per Year") +
      xlab("Release Year") +
      ylab("Avg. Cast Size") +
      xlim(1942, xcol) +
      theme(plot.title = element_text(hjust = 0.5),
            panel.background = element_blank(),
            panel.grid = element_line(color = "gray90"),
            axis.line = element_line(colour = "black"),
            plot.background = element_rect(fill = '#171614', colour = '#171614')
            
      )
    ggplotly(p + aes(text = release_year), tooltip = 'release_year')  %>% layout(plot_bgcolor = '000000')
  }
)

# mahsa

app$callback(
  output('plot_line', 'figure'),
  list(input('rating-select', 'value'),
       input('my-range-slider','value')),
  function(ratings_range,year_range) {
    df <- na.omit(data) %>% 
      filter(release_year > year_range[1],release_year < year_range[2]) %>%
      filter(rating %in% ratings_range) %>%
      group_by(release_year,rating) %>% 
      summarise(count = length(rating))
    
    plot  <- ggplot(df ,aes(x = release_year, y = count, color = rating)) +
      geom_line()+      
      scale_size(range = c(2, 12)) +
      ggtitle('Movie rating in Netflix in different years') +
      labs(x = 'Years', y= "Number of movie") +
      theme_bw() +
      theme(text =  element_text(size = 10)) +
      ggthemes::scale_color_tableau() 
    
    ggplotly(plot)
  }
)

# sufang callback

app$callback(
  output('plot-area', 'figure'),
  list(input('year3', 'value'),
       input('duration3', 'value')),
  function(year_range,duration_range){
    data_filt <- suf_data %>%
      dplyr::filter((release_year > year_range[1] & release_year < year_range[2]) & (duration > duration_range[1] & duration < duration_range[2]))
    p1 <- ggplot(data_filt, aes(x = forcats::fct_infreq(country)),text = name) +
      geom_bar(stat = 'count', color = "black", fill = "red2") +
      ggtitle('Which Countries Make the Most Movies?') +
      labs(x = 'Country') +
      labs(y = 'Number of Movies Produced')+
      theme_classic() +
      ggthemes::scale_color_tableau()+
      theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
    ggplotly(p1)
  }
)


# app$run_server(host = '0.0.0.0')
app$run_server(debug = T) # use when running locally

# work on 
library(dash)
library(dashCoreComponents)

Attaching package: ‘dashCoreComponents’

The following objects are masked from ‘package:dash’:

    dccChecklist, dccConfirmDialog, dccConfirmDialogProvider, dccDatePickerRange, dccDatePickerSingle,
    dccDropdown, dccGraph, dccInput, dccInterval, dccLink, dccLoading, dccLocation, dccLogoutButton,
    dccMarkdown, dccRadioItems, dccRangeSlider, dccSlider, dccStore, dccTab, dccTabs, dccTextarea, dccUpload

library(ggplot2)
library(plotly)

Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     
Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio

Attaching package: ‘plotly’

The following object is masked from ‘package:ggplot2’:

    last_plot

The following object is masked from ‘package:stats’:

    filter

The following object is masked from ‘package:graphics’:

    layout

library(purrr)
 
# drop down list
drop_list <- list(
    "TV-G",
    "TV-14",
    "TV-MA",
    "TV-PG",
    "R",
    "TV-Y7",
    "TV-Y",
    "PG",
    "G",
    "PG-13",
    "NR",
    "UR",
    "TV-Y7-FV",
    "NC-17")

data = read.csv(file = "data/processed/clean_df.csv")


app <- Dash$new(external_stylesheets = dbcThemes$BOOTSTRAP)

app$layout(
  dbcContainer(
    list(
      dccGraph(id='plot_line'),
      dccDropdown(
        id='rating-select',
        options = drop_list %>%  purrr::map(function(rating,pop) list(label = rating, value = rating)) , 
        value=list("TV-G","TV-MA", "TV-14","TV-Y7"),
        multi=TRUE),
      dccRangeSlider(
        id='my-range-slider',
        min=1942,
        max=2020,
        marks = list(
          '1942' = '1942',
          '1960' = '1960',
          '1980' = '1980',
          '2000' = '2000',
          '2020' = '2020'
          
        ),
        value=list(2003, 2020)
      )
    )
  )
)

app$callback(
  output('plot_line', 'figure'),
  list(input('rating-select', 'value'),
       input('my-range-slider','value')),
  function(ratings_range,year_range) {
    df <- na.omit(data) %>% 
      filter(release_year > year_range[1],release_year < year_range[2]) %>%
      filter(rating %in% ratings_range) %>%
      group_by(release_year,rating) %>% 
      summarise(count = length(rating))
    
    plot  <- ggplot(df ,aes(x = release_year, y = count, color = rating)) +
      geom_line()+      
      scale_size(range = c(2, 12)) +
      ggtitle('Movie rating in Netflix in different years') +
      labs(x = 'Years', y= "Number of movie") +
      theme_bw() +
      theme(text =  element_text(size = 10)) +
      ggthemes::scale_color_tableau() 
    
    ggplotly(plot)
  }
)

app$run_server(debug = T) # use when running locally

⚠ No source directory information available; hot reloading has been disabled.
Please ensure that you are loading your Dash for R application using source().

⚠ Note: As of version 1.0, the following packages are deprecated and should no longer be installed or loaded
when using Dash for R: `dashHtmlComponents`, `dashCoreComponents`, `dashTable`. These components are now
bundled within the `dash` package.
Fire started at 127.0.0.1:8050

start: 127.0.0.1:8050

NA

stop: 127.0.0.1:8050

drop_list <- list("TV-G",
                  "TV-14",
                  "TV-MA"
                  )
options <- drop_list %>%  purrr::map(function(rating,pop) list(label = rating, value = rating))

options

[[1]]
[[1]]$label
[1] "TV-G"

[[1]]$value
[1] "TV-G"


[[2]]
[[2]]$label
[1] "TV-14"

[[2]]$value
[1] "TV-14"


[[3]]
[[3]]$label
[1] "TV-MA"

[[3]]$value
[1] "TV-MA"

# testing <- data |>
#   select(rating, title) |>
#   group_by(rating) 
# 
# 
# str(data$rating)
# drop_list <- list("TV-G",
#                   "TV-14",
#                   "TV-MA"
#                   )